Multi-Class Ada-Boost Classification of Object Poses through Visual and Infrared Image Information Fusion

Paper i proceeding, 2012

This paper presents a novel method for pose classification using fusion of visual and thermal infrared(IR) images. We propose a novel tree structure multi-class classification scheme with visual and IR sub-classifiers. These sub-classifiers are different from the conventional one-against-all or one-against-one strategies, where we handle the multi-class problem directly. We propose to use an accuracy score for the fusion of visual and IR subclassifiers. In addition, we propose to use the original Haar features plus an extra one, and a multi-threshold weak learner to obtain weak hypothesis. The experimental results on a visual and IR image dataset containing 3018 face images in three poses show that the proposed classifier achieves high classification rate of 99.50% on the test set. Comparisons are made to a fused one-vs-all method, a classifier with visual band only, and a classifier with IR band only. Results provide further support to the proposed method.